AWS Machine Learning Blog

Category: Artificial Intelligence

Reduce ML inference costs on Amazon SageMaker for PyTorch models using Amazon Elastic Inference

Today, we are excited to announce that you can now use Amazon Elastic Inference to accelerate inference and reduce inference costs for PyTorch models in both Amazon SageMaker and Amazon EC2. PyTorch is a popular deep learning framework that uses dynamic computational graphs. This allows you to easily develop deep learning models with imperative and […]

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Building an AI-powered Battlesnake with reinforcement learning on Amazon SageMaker

Battlesnake is an AI competition based on the traditional snake game in which multiple AI-powered snakes compete to be the last snake surviving. Battlesnake attracts a community of developers at all levels. Hundreds of snakes compete and rise up in the ranks in the online Battlesnake global arena. Battlesnake also hosts several offline events that […]

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Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker

Amazon SageMaker enables organizations to build, train, and deploy machine learning models. Consumer-facing organizations can use it to enrich their customers’ experiences, for example, by making personalized product recommendations, or by automatically tailoring application behavior based on customers’ observed preferences. When building such applications, one key architectural consideration is how to make the runtime inference […]

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Training batch reinforcement learning policies with Amazon SageMaker RL

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. In addition to building ML models using more commonly used supervised and unsupervised learning techniques, you can also build reinforcement learning (RL) models using Amazon SageMaker RL. […]

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The AWS DeepRacer League: The 2020 season is underway as the Virtual Circuit opens for racing!

The AWS DeepRacer League is the world’s first autonomous racing league and is open to anyone. In 2019, tens of thousands of developers from around the world took part in a race against the clock to see who had the fastest model. The competition ended with SOLA from Japan taking home the grand prize and […]

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Using DeepChem with Amazon SageMaker for virtual screening

Virtual screening is a computational methodology used in drug or materials discovery by searching a vast amount of molecules libraries to identify the structures that are most likely to show the target characteristics. It is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of […]

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Optimizing application performance with Amazon CodeGuru Profiler

Amazon CodeGuru (Preview) is a service launched at AWS re:Invent 2019 that analyzes the performance characteristics of your application and provides automatic recommendations on ways to improve. It does this by profiling your application’s runtime (with CodeGuru Profiler) and by automatically reviewing source code changes (with CodeGuru Reviewer). For more information, see What Is Amazon […]

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Automating your Amazon Forecast workflow with Lambda, Step Functions, and CloudWatch Events rule

Amazon Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts without requiring any prior ML experience. Forecast is applicable in a wide variety of use cases, including estimating product demand, energy demand, workforce planning, computing cloud infrastructure usage, traffic demand, supply chain optimization, and financial planning. Forecast is […]

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Winners of AWS Machine Learning Research Awards announced

The AWS Machine Learning Research Awards (MLRA) provides unrestricted cash funds and AWS Promotional Credits to academics to advance the frontiers of machine learning (ML) and its applications. MLRA is pleased to announce winners for its 2019 Q2/Q3 call-for-proposal cycles: Mohit Bansal, University of North Carolina Chapel Hill, Auto-Adversarial Training to Make Dialogue Systems Robust […]

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Customers Achieve Machine Learning Success with AWS’s Machine Learning Solutions Lab

AWS introduced the Machine Learning (ML) Solutions Lab a little over two years ago to connect our machine learning experts and data scientists with AWS customers.  Our goal was to help our customers solve their most pressing business problems using ML. We’ve helped our customers increase fraud detection rates, improved forecasting and predictions for more […]

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